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Automated sequence of processes that ingest, transform, and deliver data from source systems to target destinations.
DESCRIPTION
A data pipeline moves data reliably between systems for analytics and operations. It orchestrates ingestion, transformation, validation, and loading tasks. Pipelines may be batch or streaming, supporting latency and throughput requirements. Robust pipelines include error handling, retries, monitoring, and lineage. Design choices consider scalability, idempotency, schema evolution, and data contracts.
ALTERNATIVE DEFINITIONS
Google Cloud
A data pipeline is a series of processes that move data from one system to another.
Simple framing of movement across systems.
AWS
A data pipeline is a set of actions that ingest, process, and move data from one system to another.
Highlights ingest and process actions in sequence.
Microsoft Azure Architecture Center
A data pipeline is an end-to-end sequence of data processing steps that reliably transport data between systems.
Focus on end-to-end reliability and processing steps.
RELATED TERMS
Extract-Transform-Load ELT Orchestration Streaming Change Data Capture Workflow Apache Airflow Apache Kafka Data Integration
COMPETING TERMS
Batch Processing Stream Processing
RELATIONS
- Narrower: Aggregation Pipeline
KNOWLEDGE AREAS
Data Engineering Data Integration & Interoperability Real-time & Streaming Data Data Storage & Operations
TARGET AUDIENCE
Data Engineer Analytics Engineer Software Developer MLOps Engineer Cloud Architect
EXTERNAL SOURCES
- Wikipedia - Data pipeline
- Google Cloud - Overview and key components
- AWS - AWS perspective and examples
- Microsoft - Azure architecture guidance
TOP QUESTIONS
What distinguishes batch and streaming Data Pipelines?
Batch pipelines process data in chunks on schedules; streaming pipelines process events continuously with low latency, suiting real-time use cases.
Category: Comparison
What are key reliability features in a Data Pipeline?
Idempotent operations, retries, dead-letter queues, monitoring, alerting, and data validation ensure resilience and correctness.
Category: Requirements
What is a data contract in a Data Pipeline?
A data contract defines schemas, semantics, SLAs, and quality requirements between producers and consumers to reduce breakage.
Category: Clarification
What mistakes harm Data Pipeline scalability?
Stateful steps without scaling, tight coupling, poor partitioning, and ignoring backpressure lead to bottlenecks and failures.
Category: Mistakes
Why is observability important for Data Pipelines?
Metrics, logs, and traces track health and performance, enabling rapid issue detection and recovery.
Category: Relevancy
What’s a hot topic in Data Pipelines today?
Declarative pipelines and metadata-driven orchestration enable flexibility, reuse, and governance.
Category: Topic
How do schemas evolve in Data Pipelines?
Use versioning, backward compatibility, feature flags, and schema registries to manage changes safely.
Category: BestPractice
What tools are commonly used for Data Pipelines?
Airflow, Prefect, Dagster, Kafka/Flink/Spark for streaming, and dbt for transformations.
Category: Example
Where to start building a Data Pipeline?
Define sources and targets, model schemas, design transformations, then implement with orchestration, tests, and monitoring.
Category: Learning